Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa

Jantz, Samuel M.; Pintea, Lilian; Nackoney, Janet; Hansen, Matthew C.

doi:10.3390/rs8050427

Cited by 28 publications

(28 citation statements)

References 43 publications

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“…Expansion of logging into intact forest areas has many direct effects on ecosystem functions, including reduction of carbon storage ( 20 ), decrease of habitat suitability ( 6 , 21 ), and increase of vulnerability to human-induced wildfires ( 22 , 23 ). Fragmentation of forest landscapes by logging and logging roads causes direct habitat loss ( 24 ) and increases the incidence of poaching ( 25 ), resulting in species loss.…”

Section: Discussionmentioning

confidence: 99%

The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013

Potapov

Hansen

Laestadius³

et al. 2017

Sci. Adv.

587

540

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Section: Discussionmentioning

confidence: 99%

The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013

Potapov

Hansen

Laestadius³

et al. 2017

Sci. Adv.

587

540

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“…) has so far not been fully exploited (but see Jantz et al. ). The approach introduced in this study can help bridging this gap, opening up new possibilities to monitor spatiotemporal habitat dynamics based on Landsat imagery.…”

Section: Discussionmentioning

confidence: 99%

“…This offers great flexibility in terms of generating consistent predictors for habitat modeling across space and time and matching predictor data with available species records. This extraordinary potential for assessing habitat across large areas (Hansen and Loveland 2012) and long time periods (Kerr and Ostrovsky 2003;Vogelmann et al 2012;Kennedy et al 2014) has so far not been fully exploited (but see Jantz et al 2016). The approach introduced in this study can help bridging this gap, opening up new possibilities to monitor spatiotemporal habitat dynamics based on Landsat imagery.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the combination of many Landsat images in space or time can imply considerable technical challenges and computational costs (Young et al 2017). While time series-based approaches have been widely adopted for characterizing habitat with coarse resolution sensors such as MODIS (Coops et al 2009;Cord and R€ odder 2011;Bischof et al 2012), Landsat-based habitat studies instead have frequently relied on images-based analysis of only a single or few selected images (Lahoz-Monfort et al 2010; but see Jantz et al 2016;Remelgado et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

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“…Suitable chimpanzee habitat was modeled using Landsat data for each of the four populations that cover its current range [13]. In a boreal study of full-range (380-2500 nm) hand-held spectral reflectance of mammal pelts [14] showed…”

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confidence: 99%

Preface: Remote Sensing of Biodiversity

Ustin¹

2016

Remote Sensing

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Since the 1992 Earth Summit in Rio de Janeiro, the importance of biological diversity in supporting and maintaining ecosystem functions and processes has become increasingly understood [1]. Biodiversity "connects the web of life," that is, biodiversity represents the diversity of species in an ecosystem, landscape, region and globe. It is their combined interactions, with each other and their environment that alters biogeochemical cycles and the climate system. In recent years, biodiversity has come to broadly include diversity in terms of taxonomic, systematic and genetic attributes, morphological and structural attributes, and their ecological and functional traits. Human actions are driving climate change and land use changes throughout the globe, causing major losses of biodiversity [2,3]. These actions impact the climate, influencing magnitude and the timing of disturbance events, e.g., drought and wildfires, create pollution and contamination in water, air, and soil, and cause many other impacts that accelerate species losses, altering ecosystem functions and their services. The loss of biological diversity impacts ecological processes at scales comparable to other drivers of global environmental change [4] and likely interacts synergistically with climate change [5]. Biodiversity is recognized as a key factor in the maintenance of healthy ecosystems and for the sustainability of conservation efforts. Losses of biodiversity reduce the stability and resilience of ecosystems through the loss of functional traits associated with resource capture and decomposition [1]. The rapid pace of global change requires increased knowledge about species composition, numbers of species, and the states of health and the conditions for global ecosystems in order to respond effectively. Tittensor et al. (2014) [6] show that many of the 20 Aichi indicator targets from the Convention on Biological Diversity are unlikely to be met by 2020. The development of large plant trait databases like TRY [7,8] that have data on thousands of vascular plant species (46,085 species) still remain significantly under-sampled, particularly outside the northern temperate zone [9]. Remote sensing provides the only feasible way to measure and monitor biodiversity changes at the scales necessary. Nonetheless, until recently, there has been little success in monitoring ecologically meaningful aspects of diversity (e.g., alpha, beta, and gamma diversity). Today, there are an increasing number of remote sensing satellites and aircraft instruments that can provide a wide range of observational capability, in terms of spatial, temporal, and spectral resolutions, especially when combined with "big data" computational capacity and in situ monitoring systems. Similarly, significant progress in image processing algorithms has increased the potential for the successful characterization of biodiversity at various scales.We are pleased to present this special issue of 18 state-of-the-science papers [10-27] covering a wide range biological diversity issues ass...

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Landsat ETM+ and SRTM Data Provide Near Real-Time Monitoring of Chimpanzee (Pan troglodytes) Habitats in Africa

Cited by 28 publications

References 43 publications

The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013

The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Preface: Remote Sensing of Biodiversity

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